Advancing hydrological modeling in the Mediterranean: Multi-objective calibration of the SWAT+ model using open-source data and tools

Abstract

In Mediterranean and semi-arid regions, climate change imposes significant pressure on water resources and hydrological systems. Accurate simulation and measurement of water availability require precise hydrological models, usually calibrated using streamflow data. However, the increasing availability of remote sensing data, such as evapotranspiration (ET), offers new opportunities to enhance calibration, particularly in data-scarce regions. This study used the SWAT+ model, implemented via the R-SWAT parallel computing framework, to analyze calibration strategies in the Upper Oum Er Rbia watershed, Morocco. Two sensitivity analyses were conducted to identify parameters affecting streamflow and ET, which were then used to evaluate five calibration strategies using streamflow observations and GLDAS ET data. Results showed that the single-variable calibrations achieved high Q or ET performance but reduced performance in the other variable. In contrast, a multi-variable calibration approach (Multi_Q&ET) optimized streamflow and ET, achieving satisfactory results. Streamflow calibration and validation performance metrics were NSE = 0.75 and 0.67, KGE = 0.77 and 0.70, and PBIAS = 19.2% and 9.1%. For ET, calibration and validation yielded NSE = 0.51 and 0.55, KGE = 0.64 and 0.71, and PBIAS = 5.0% and 2.9%. While slightly less accurate than single-objective calibrations, the multi-variable approach achieved balanced and acceptable performance across variables. These findings highlight the significant contribution of open-access remote sensing evapotranspiration data in refining model parameters, offering a realistic and effective strategy to enhance hydrological model calibration. This approach is particularly valuable for improving water resource management in regions vulnerable to climate change and limited by observational data. • Integrating GLDAS evapotranspiration (ET) data improves hydrological model calibration. • Multi-objective calibration in SWAT+ balances accuracy across variables. • Parallel computing via R-SWAT enhances calibration efficiency, enabling robust sensitivity analysis. • RS data improves hydrological modeling offering a scalable solution for water management.